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GenBot, Advanced RAG-Based Web Chatbot with CrewAI

Overview

An intelligent web chatbot that combines stored knowledge, vector search, and live web results to deliver more accurate RAG-based answers.

Many websites and businesses need a chatbot that can answer questions from stored knowledge as well as search the web when updated information is needed. A normal chatbot often gives generic answers, but GenBot is designed to provide more accurate and context-aware responses using a RAG-based architecture.

This project builds a pipeline that processes unstructured data such as JSON files, converts the content into embeddings using OpenAI, and stores them inside ChromaDB for fast retrieval. When a user asks a question, the chatbot searches the stored knowledge base, combines it with web search when needed, and generates a more useful answer.

With agent-based workflows, GenBot improves how information is collected, fused, and delivered. The system focuses on better response accuracy, real-time output quality, and a smooth web-based chatbot experience through Streamlit.

Key benefits

  1. Accurate chatbot answers using RAG-based retrieval
  2. Converts unstructured data into searchable vector knowledge
  3. Web search integration for updated information
  4. Improves chatbot output through knowledge fusion
  5. Simple Streamlit-based chatbot interface

What's included

  1. Data vectorization pipeline for unstructured files
  2. ChromaDB-based knowledge storage
  3. RAG chatbot response system
  4. Web search integration
  5. Streamlit chatbot interface

Use Cases

  1. Website AI chatbot
  2. Business knowledge assistant
  3. JSON or document-based question answering
  4. Web search-enabled chatbot
  5. Customer support automation

Key deliverables

  1. RAG-based chatbot system
  2. OpenAI embedding pipeline
  3. ChromaDB vector storage setup
  4. LangChain-powered response workflow
  5. Web search integration
  6. Streamlit web chatbot interface
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